Scheduling mechanisms to control the spread of COVID-19

We study scheduling mechanisms that explore the trade-off between containing the spread of COVID-19 and performing in-person activity in organizations. Our mechanisms, referred to as group scheduling, are based on partitioning the population randomly into groups and scheduling each group on appropriate days with possible gaps (when no one is working and all are quarantined). Each group interacts with no other group and, importantly, any person who is symptomatic in a group is quarantined. We show that our mechanisms effectively trade-off in-person activity for more effective control of the COVID-19 virus spread. In particular, we show that a mechanism which partitions the population into two groups that alternatively work in-person for five days each, flatlines the number of COVID-19 cases quite effectively, while still maintaining in-person activity at 70% of pre-COVID-19 level. Other mechanisms that partitions into two groups with less continuous work days or more spacing or three groups achieve even more aggressive control of the virus at the cost of a somewhat lower in-person activity (about 50%). We demonstrate the efficacy of our mechanisms by theoretical analysis and extensive experimental simulations on various epidemiological models based on real-world data.

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Additional Editor Comments: To Corresponding Author, I require before a possible acceptance that are followed all the modifications suggested by the referees. So, I recommend you to read the reports and make the changes. 1. Is the manuscript technically sound, and do the data support the conclusions?
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In the real world, it may be useful to highlight the possible different effects of the group scheduling proposed to avoid contagion in different situations whereas the usual cyclical mechanisms typically do not. In the transmission of the virus must also be considered the possible influences determined by the behaviours more or less correct observed by the subjects. The authors have shown that Model 2.5.0 compared to Model 1.5.2, drastically reduces the number of peak infections and significantly the total number of infections in the population, but results in a 30% reduction in the work ratio. The proposed scheme would be able to achieve the result of reducing the transmission of the virus and reducing the contagion in health facilities or hospitals, even where the behaviours have not substantially changed a cause of pandemic (D).
We thank the reviewer for the insight regarding health facilities and hospitals and have updated the manuscript accordingly, as well as adding the suggested citation.
This is why the model could usefully be proposed in the construction of shifts of health workers. In the healthcare facilities, bearing the alternation provided for in the group scheduling mechanism presupposes a workforce that is numerically adequate to the standard needs. The latter could, with smaller health workers' numbers, in the event of a pandemic, cope with a greater activity with sacrifice but would allow to continue to safeguard health and provide essential services.
The identification of the three parameters for the identification of the group scheduling mechanism is clear. In my opinion, Table 1 does not add anything to what is described in the text. I would suggest to files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free.
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